Learning an interpretable and disentangled representation of data to reflect the semantic meaning is what machine learning always pursues [1]}, [2]}, [3]}, [4]}. Disentangled representation is defined in [1]} as: a representation where a change in one dimension corresponds to a change in one factor of variation, while being relatively invariant to changes in other factors. As far as our understanding is concerned, the fact that different dimensions do not affect each other means probabilistically independent.
